{"id":"W7078264798","doi":"10.1016/j.apr.2025.102702","title":"Integration of Sentinel-5P satellite data and machine learning for spatiotemporal prediction of NO2 in Delhi: Impacts of COVID-19 lockdown","year":2025,"lang":"en","type":"article","venue":"Atmospheric Pollution Research","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Support vector machine; Decision tree; Random forest; Regression; Mean squared error; Regression analysis; Air quality index; Satellite; Linear regression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002342347,0.00007546498,0.0001832607,0.00003877739,0.00008090666,0.0000174025,0.0004324085,0.00008380115,0.000008972003],"category_scores_gemma":[0.002504652,0.00007023766,0.00002509596,0.0009282594,0.0001543944,0.0002163291,0.0004054884,0.0001874978,2.165704e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005752812,"about_ca_system_score_gemma":0.0003377385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001773168,"about_ca_topic_score_gemma":0.0001355109,"domain_scores_codex":[0.9986094,0.0002189779,0.0004031598,0.0002959879,0.0002657627,0.0002067078],"domain_scores_gemma":[0.9987716,0.0002960293,0.0001719675,0.0004164511,0.0002837512,0.00006019204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005774509,0.0004374974,0.6039254,0.003014608,0.000108588,0.000005029673,0.003476334,0.01501229,0.2240853,0.01514303,0.001536273,0.1326781],"study_design_scores_gemma":[0.0007778986,0.0001305024,0.09895474,0.0001368415,0.000005698077,0.000002423597,0.000279241,0.8772963,0.01382725,0.003017571,0.005512763,0.00005880765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2757927,0.00128069,0.7167203,0.004454309,0.00006407506,0.0005099487,0.00004845873,0.00003156237,0.001097926],"genre_scores_gemma":[0.9889901,0.0002307613,0.01018405,0.00002420341,0.000009446439,0.000009453753,0.0000756457,0.000001395854,0.0004749258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.862284,"threshold_uncertainty_score":0.2998483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08890831980299632,"score_gpt":0.3627249712885717,"score_spread":0.2738166514855753,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}